# import open_clip.tokenizer
# import torch
#
# from modules import sd_hijack_clip, devices
# from modules.shared import opts
#
# tokenizer = open_clip.tokenizer._tokenizer
#
#
# class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
#     def __init__(self, wrapped, hijack):
#         super().__init__(wrapped, hijack)
#
#         self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
#         self.id_start = tokenizer.encoder["<start_of_text>"]
#         self.id_end = tokenizer.encoder["<end_of_text>"]
#         self.id_pad = 0
#
#     def tokenize(self, texts):
#         assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
#
#         tokenized = [tokenizer.encode(text) for text in texts]
#
#         return tokenized
#
#     def encode_with_transformers(self, tokens):
#         # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
#         z = self.wrapped.encode_with_transformer(tokens)
#
#         return z
#
#     def encode_embedding_init_text(self, init_text, nvpt):
#         ids = tokenizer.encode(init_text)
#         ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
#         embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
#
#         return embedded
#
#
# class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
#     def __init__(self, wrapped, hijack):
#         super().__init__(wrapped, hijack)
#
#         self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
#         self.id_start = tokenizer.encoder["<start_of_text>"]
#         self.id_end = tokenizer.encoder["<end_of_text>"]
#         self.id_pad = 0
#
#     def tokenize(self, texts):
#         assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
#
#         tokenized = [tokenizer.encode(text) for text in texts]
#
#         return tokenized
#
#     def encode_with_transformers(self, tokens):
#         d = self.wrapped.encode_with_transformer(tokens)
#         z = d[self.wrapped.layer]
#
#         pooled = d.get("pooled")
#         if pooled is not None:
#             z.pooled = pooled
#
#         return z
#
#     def encode_embedding_init_text(self, init_text, nvpt):
#         ids = tokenizer.encode(init_text)
#         ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
#         embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
#
#         return embedded
